Vector databases power RAG, semantic search, recommendations, and memory. Learn how indexing, filtering, and hybrid retrieval work now.

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

A 'picker' gathers items at Amazon's Fulfilment Centre in Peterborough, central England, on November 28, 2013. 'Cyber Monday' which falls this year on Monday December 2, 2013, is expected to be the ...

If you’re building generative AI applications, you need to control the data used to generate answers to user queries. Simply dropping ChatGPT into your platform isn’t going to work, especially if ...

Vector databases enable semantic searches but might also store sensitive data. Learn how to build a vector database security ...

Patrick Walsh is the cofounder and CEO of IronCore Labs, the data security encryption platform for software companies and AI. The proliferation of generally intelligent AI models is turning machine ...

Learn how to use vector databases for AI SEO and enhance your content strategy. Find the closest semantic similarity for your target query with efficient vector embeddings. A vector database is a ...

Vector search is nothing new. Its role as a critical data management capability, however, is a recent development due to the way it enables discovering data needed to inform generative AI models.